Based on SAP HANA SPS 12 (HCP trial)
Updated 18.04.2021 – fixed some typos, converted to WordPress Gutenberg blocks.
When Jody Hesch recently send me an email with a question about HANA, he received what everyone receives when sending email questions about HANA. The reply is a version of
“Usually I don’t do email Q&A as this simply doesn’t help with knowledge sharing.
Instead, I advise everyone to post the question in one of the HANA related forums
(SAP Community https://answers.sap.com/questions/metadata/23925/sap-hana.html ,
JAM https://jam4.sapjam.com/groups/about_page/6UHzR2Fxra4quFAbACtxFD(SAP internal)
or even StackOverflow http://stackoverflow.com/questions/tagged/hana ) so that the question and its answers are search- and findable.
That way everyone can benefit from this and you even might get faster and/or better answers than from just writing to me.”
I have not given up on my personal fight against GEIGOKAI and this is what it looks like in daily real life.
You might be inclined to call this reply rude but it turns out that most people accept this very easily and manage to copy&paste their question into one of the forums – and changing culture is always a bit painful … drip drip drip
And just as many others, Jody managed to post his question for all to see, read and discuss here: Manually managing Delta Merge
While I was doing the due diligence of checking my own ideas against a HCP trial HANA instance (SPS 12.04), the whole explanation and the example got larger than what would be nice for a simple answer, which is why you now read this is a blog post format.
On loading data and merging
Ok, let’s get to it, then.
Jody’s question was “how can manually handling delta merge make a data load faster?”
To answer this I set up a small test case:
drop table merge_test; CREATE COLUMN TABLE "MERGE_TEST" ("ID" INTEGER , "AAA" NVARCHAR(20), "BBB" INTEGER , PRIMARY KEY ("ID")); do begin declare ts_start timestamp = current_timestamp; declare ts_end timestamp; declare loops integer = 100; declare j integer; declare i integer; declare with_commit integer = 1; truncate table merge_test; for j in 0 .. :loops do for i in 0 .. :loops*50 do upsert merge_test values (:i, :i || ' - ' || :j, :i*:j) with primary key; end for; if with_commit = 1 then commit; end if; end for; ts_end = current_timestamp; -- now show some infos select seconds_between(:ts_start, :ts_end) as duration from dummy; select count(*) as num_rows, min(id), max(id) from merge_test; select column_name, memory_size_in_main, memory_size_in_delta, count, distinct_count from m_cs_all_columns where table_name ='MERGE_TEST' and schema_name =current_user; select merge_count, read_count, write_count, last_merge_time from m_cs_tables where table_name ='MERGE_TEST' and schema_name =current_user; select start_time, motivation, execution_time, memory_merge, merged_delta_records, success from m_delta_merge_statistics where table_name='MERGE_TEST' and start_time >= :ts_start and schema_name = current_user; end;
What we have here are a simple table and a data loading simulation. The data gets inserted or updated via the UPSERT command and if the parameter with_commit equals 1 a commit is done after each iteration of the outer loop.
After both loops have been exited, the code prints the runtime in seconds and selects some statistics from system views.
Next, I ran three experiments.
The first two used the SAP HANA AUTOMERGE feature, where HANA decides when to perform a delta merge.
The third one followed the approach SAP BW uses for data loading: the AUTOMERGE is disabled and the target tables get merged after all data has been loaded.
No commit, AUTOMERGE ON
DURATION 386 NUM_ROWS MIN(ID) MAX(ID) 5001 0 5000 COLUMN_NAME MEMORY_SIZE_IN_MAIN MEMORY_SIZE_IN_DELTA COUNT DISTINCT_COUNT ID 1064 3733044 505101 5001 AAA 0 17607386 505101 -1 BBB 0 5571068 505101 -1 $trex_udiv$ 232 8889016 0 0 $rowid$ 768 15676428 505101 505101 MERGE_COUNT READ_COUNT WRITE_COUNT LAST_MERGE_TIME 0 7 505101 2016-12-20 03:15:41.986
Easy to see, the whole thing ran for 386 seconds and managed to create a total of 505.101 record versions in the delta store.
Note that during this load NO delta merge had happened. This is because we never committed the transaction at all and the delta merge won’t “relocate” open update transactions to a new delta store.
Commit, AUTOMERGE ON
DURATION 349 NUM_ROWS MIN(ID) MAX(ID) 5001 0 5000 COLUMN_NAME MEMORY_SIZE_IN_MAIN MEMORY_SIZE_IN_DELTA COUNT DISTINCT_COUNT ID 723716 9132 5001 5001 AAA 741076 10052 5001 5001 BBB 711828 9132 5001 5001 $trex_udiv$ 273432 1912 0 0 $rowid$ 1504 9132 5001 5001 MERGE_COUNT READ_COUNT WRITE_COUNT LAST_MERGE_TIME 5 30 505111 2016-12-20 03:40:22.85 START_TIME MOTIVATION EXECUTION_TIME MEMORY_MERGE MERGED_DELTA_RECORDS SUCCESS 2016-12-20 03:36:22.166 AUTO 54 FALSE 80016 TRUE 2016-12-20 03:37:22.24 AUTO 88 FALSE 90018 TRUE 2016-12-20 03:38:22.349 AUTO 119 FALSE 85017 TRUE 2016-12-20 03:39:22.49 AUTO 157 FALSE 85017 TRUE 2016-12-20 03:40:22.67 AUTO 186 FALSE 85017 TRUE
Here we see that with commits in between the whole thing only took 349 seconds.
As we’ve committed the transaction in between a couple of times, the automerge had a chance to be active – and did so roughly every minute (5 times).
Now, why is this variant a bit faster overall? The effort to find the currently active record versions grows with the number of versions in the delta store.
With the automerge in between, this number stayed consistently below 100.000 versions, while the first version had to face an increasing number of versions to check between round 100.000 and 505.101.
Commit, AUTOMERGE OFF
Finally the version of data loading similar to SAP BW’s approach.
I disabled the automerge via
alter table merge_test disable automerge;
and ran the test again:
DURATION 325 NUM_ROWS MIN(ID) MAX(ID) 5001 0 5000 COLUMN_NAME MEMORY_SIZE_IN_MAIN MEMORY_SIZE_IN_DELTA COUNT DISTINCT_COUNT ID 1064 3733044 505101 5001 AAA 0 17607386 505101 -1 BBB 0 5571068 505101 -1 $trex_udiv$ 232 8810536 0 0 $rowid$ 768 15676428 505101 505101 MERGE_COUNT READ_COUNT WRITE_COUNT LAST_MERGE_TIME 0 15 505101 2016-12-20 03:49:36.914 START_TIME MOTIVATION EXECUTION_TIME MEMORY_MERGE MERGED_DELTA_RECORDS SUCCESS merge delta of merge_test; START_TIME MOTIVATION EXECUTION_TIME MEMORY_MERGE MERGED_DELTA_RECORDS SUCCESS 2016-12-20 03:56:09.435 HARD 46 FALSE 5001 TRUE
With 325 seconds this is the fastest run.
Looking at the time the manual delta merge took, we find that it’s still faster than the last delta merge of the automerge example.
This is – again – due to the fact that now there are a lot fewer concurrent record versions in the table.
Note, MERGED_DELTA_RECORDS shows the number of valid (committed) records that have been moved from delta to main storage.
If I update the same record a hundred times before I commit, then I would only get one record merged into the main store.
Now, why do we see a better performance in this case?
To illustrate the resource usage during our loads, see the following diagrams:
In our first case, we see that the memory usage for the delta store as well as the CPU usage for the UPSERT gets larger over time.
Sometime after the last commit is done, an automerge gets triggered, leading to additional CPU and a lot more memory usage.
The result is a relatively well-compressed table (last little blue square).
Note that the number of uncomitted record versions (little orange squares) increases during the whole time.
With the second approach (commits in between and automerge on), we see that CPU and memory usage during the load stay moderate. Only when the merge is performed, memory and CPU usage increase and the whole process has to wait for the merge to switch over to the delta2 in order to continue.
Noteworthy here is that the resulting table can easily be larger than the table sizes produced by the other methods. This is because the column compression algorithms are determined during the first delta merge and won’t change that easily.
This can lead to a situation where the compression algorithms are determined on a subset of data that is not representative of the total (think of loading data with timestamps or dates in sequential order – the first merge may only see one specific date).
With the last option, we see a pattern very similar to the first case.
The only difference here is that the final merge was manually triggered.
So with AUTOMERGE disabled and regular commits we get the best of everything:
- no waits due to delta merges
- no increased runtimes due to a high number of outdated uncommitted record versions
- well-compressed table (since the compression optimisation can work on the whole data set).
This works well, as long as the resulting delta merge can be done within the available memory.
And even if other memory structures would need to be unloaded to allow for the delta merge to happen, then this would have to happen only once and not several times during the loading process.
I mentioned that this third approach is similar to what SAP BW does but this is only half the truth.
Instead of manually sending a
merge delta of merge_test;
which gives the hard instruction to perform a delta merge now, SAP BW uses a “smart merge”.
The idea here is that instead of forcing a delta merge to let HANA evaluate whether a merge would be granted for, given the current memory statistics and given the merge decision functions.
With this flexible approach, the application does not need to care about the specifics of how to decide when a merge should take place, but can yet trigger HANA to take care about that.
Manually controlling the delta merge can improve performance for the load process as well as reduce the overall system load during the data loading, since multiple merges are avoided.
Delta merges still are extremely resource-intensive operations that can have a massive impact on the whole HANA system. Therefore it’s a good idea to have them done as seldom as possible.
This, in combination with a controlled approached to COMMITTING loaded data can, as shown above, have quite an effect on the runtime of the data loading.
There you go, now you know.